import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
      import PIL.Image

      from .features import FeatureType


SCREAMING_SNAKE_CASE__		:       Optional[List[str]]       						=       None
SCREAMING_SNAKE_CASE__		:       Union[str, Any]       						=       "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
SCREAMING_SNAKE_CASE__		:       Optional[Any]       						=       [
    np.dtype("|b1"),
    np.dtype("|u1"),
    np.dtype("<u2"),
    np.dtype(">u2"),
    np.dtype("<i2"),
    np.dtype(">i2"),
    np.dtype("<u4"),
    np.dtype(">u4"),
    np.dtype("<i4"),
    np.dtype(">i4"),
    np.dtype("<f4"),
    np.dtype(">f4"),
    np.dtype("<f8"),
    np.dtype(">f8"),
]

@dataclass
class    snake_case       :
  lowercase_								=  True
  lowercase_								=  None
  # Automatically constructed
  lowercase_								=  "PIL.Image.Image"
  lowercase_								=  pa.struct({'bytes': pa.binary(), 'path': pa.string()}    )
  lowercase_								=  field(default='Image' ,    init=UpperCamelCase_ ,    repr=UpperCamelCase_    )




  def __call__(   self  :						str					)->							Dict:




       """simple docstring"""
       return self.pa_type




  def 						__lowercase(   self  :						str  ,			a_  :						Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]					)->							dict:




       """simple docstring"""
       if config.PIL_AVAILABLE:
            import PIL.Image
       else:
            raise ImportError('To support encoding images, please install \'Pillow\'.'					)

       if isinstance(a_  ,			a_					):
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		np.array(a_					)

       if isinstance(a_  ,			a_					):
            return {"path": value, "bytes": None}
       elif isinstance(a_  ,			a_					):
            return {"path": None, "bytes": value}
       elif isinstance(a_  ,			np.ndarray					):
            # convert the image array to PNG/TIFF bytes
            return encode_np_array(a_					)
       elif isinstance(a_  ,			PIL.Image.Image					):
            # convert the PIL image to bytes (default format is PNG/TIFF)
            return encode_pil_image(a_					)
       elif value.get('path'					) is not None and os.path.isfile(value['path']					):
            # we set "bytes": None to not duplicate the data if they're already available locally
            return {"bytes": None, "path": value.get('path'					)}
       elif value.get('bytes'					) is not None or value.get('path'					) is not None:
            # store the image bytes, and path is used to infer the image format using the file extension
            return {"bytes": value.get('bytes'					), "path": value.get('path'					)}
       else:
            raise ValueError(
                F'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''					)




  def 						__lowercase(   self  :						Union[str, Any]  ,			a_  :						dict  ,			a_  :						str=None					)->							"PIL.Image.Image":




       """simple docstring"""
       if not self.decode:
            raise RuntimeError('Decoding is disabled for this feature. Please use Image(decode=True) instead.'					)

       if config.PIL_AVAILABLE:
            import PIL.Image
       else:
            raise ImportError('To support decoding images, please install \'Pillow\'.'					)

       if token_per_repo_id is None:
            SCREAMING_SNAKE_CASE__					:					List[str]      =		{}

       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		value['path'], value['bytes']
       if bytes_ is None:
            if path is None:
                 raise ValueError(F'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.'''					)
            else:
                 if is_local_path(a_					):
                      SCREAMING_SNAKE_CASE__					:					Any      =		PIL.Image.open(a_					)
                 else:
                      SCREAMING_SNAKE_CASE__					:					str      =		path.split('::'					)[-1]
                      try:
                           SCREAMING_SNAKE_CASE__					:					str      =		string_to_dict(a_  ,			config.HUB_DATASETS_URL					)['repo_id']
                           SCREAMING_SNAKE_CASE__					:					Tuple      =		token_per_repo_id.get(a_					)
                      except ValueError:
                           SCREAMING_SNAKE_CASE__					:					Tuple      =		None
                      with xopen(a_  ,			'rb'  ,			use_auth_token=a_					) as f:
                           SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		BytesIO(f.read()					)
                      SCREAMING_SNAKE_CASE__					:					str      =		PIL.Image.open(bytes_					)
       else:
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		PIL.Image.open(BytesIO(bytes_					)					)
       image.load()  # to avoid "Too many open files" errors
       return image




  def 						__lowercase(   self  :						List[str]					)->							Union["FeatureType", Dict[str, "FeatureType"]]:




       """simple docstring"""
       from .features import Value

       return (
           self
           if self.decode
           else {
               "bytes": Value('binary'					),
               "path": Value('string'					),
           }
       )




  def 						__lowercase(   self  :						Optional[Any]  ,			a_  :						Union[pa.StringArray, pa.StructArray, pa.ListArray]					)->							pa.StructArray:




       """simple docstring"""
       if pa.types.is_string(storage.type					):
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		pa.array([None] * len(a_					)  ,			type=pa.binary()					)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		pa.StructArray.from_arrays([bytes_array, storage]  ,			['bytes', 'path']  ,			mask=storage.is_null()					)
       elif pa.types.is_binary(storage.type					):
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		pa.array([None] * len(a_					)  ,			type=pa.string()					)
            SCREAMING_SNAKE_CASE__					:					str      =		pa.StructArray.from_arrays([storage, path_array]  ,			['bytes', 'path']  ,			mask=storage.is_null()					)
       elif pa.types.is_struct(storage.type					):
            if storage.type.get_field_index('bytes'					) >= 0:
                 SCREAMING_SNAKE_CASE__					:					List[Any]      =		storage.field('bytes'					)
            else:
                 SCREAMING_SNAKE_CASE__					:					Dict      =		pa.array([None] * len(a_					)  ,			type=pa.binary()					)
            if storage.type.get_field_index('path'					) >= 0:
                 SCREAMING_SNAKE_CASE__					:					Tuple      =		storage.field('path'					)
            else:
                 SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		pa.array([None] * len(a_					)  ,			type=pa.string()					)
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		pa.StructArray.from_arrays([bytes_array, path_array]  ,			['bytes', 'path']  ,			mask=storage.is_null()					)
       elif pa.types.is_list(storage.type					):
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		pa.array(
                [encode_np_array(np.array(a_					)					)['bytes'] if arr is not None else None for arr in storage.to_pylist()]  ,			type=pa.binary()  ,			)
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		pa.array([None] * len(a_					)  ,			type=pa.string()					)
            SCREAMING_SNAKE_CASE__					:					Dict      =		pa.StructArray.from_arrays(
                [bytes_array, path_array]  ,			['bytes', 'path']  ,			mask=bytes_array.is_null()					)
       return array_cast(a_  ,			self.pa_type					)




  def 						__lowercase(   self  :						List[Any]  ,			a_  :						pa.StructArray					)->							pa.StructArray:




       """simple docstring"""

       @no_op_if_value_is_null
       def path_to_bytes(a_  :						Dict					):
            with xopen(a_  ,			'rb'					) as f:
                 SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		f.read()
            return bytes_

       SCREAMING_SNAKE_CASE__					:					Any      =		pa.array(
           [
               (path_to_bytes(x['path']					) if x['bytes'] is None else x['bytes']) if x is not None else None
               for x in storage.to_pylist()
           ]  ,			type=pa.binary()  ,			)
       SCREAMING_SNAKE_CASE__					:					Dict      =		pa.array(
           [os.path.basename(a_					) if path is not None else None for path in storage.field('path'					).to_pylist()]  ,			type=pa.string()  ,			)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		pa.StructArray.from_arrays([bytes_array, path_array]  ,			['bytes', 'path']  ,			mask=bytes_array.is_null()					)
       return array_cast(a_  ,			self.pa_type					)







def      _a					(   ):







     '''simple docstring'''


     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('To support encoding images, please install \'Pillow\'.'			)

     global _IMAGE_COMPRESSION_FORMATS
     if _IMAGE_COMPRESSION_FORMATS is None:
          PIL.Image.init()
          SCREAMING_SNAKE_CASE__					:					int      =		list(set(PIL.Image.OPEN.keys()			) & set(PIL.Image.SAVE.keys()			)			)
     return _IMAGE_COMPRESSION_FORMATS







def      _a					(   lowercase__	:      "PIL.Image.Image"			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					Dict      =		BytesIO()
     if image.format in list_image_compression_formats():
          SCREAMING_SNAKE_CASE__					:					int      =		image.format
     else:
          SCREAMING_SNAKE_CASE__					:					Tuple      =		'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
     image.save(lowercase__  ,	format=lowercase__			)
     return buffer.getvalue()







def      _a					(   lowercase__	:      "PIL.Image.Image"			):







     '''simple docstring'''


     if hasattr(lowercase__  ,	'filename'			) and image.filename != "":
          return {"path": image.filename, "bytes": None}
     else:
          return {"path": None, "bytes": image_to_bytes(lowercase__			)}







def      _a					(   lowercase__	:      np.ndarray			):







     '''simple docstring'''


     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('To support encoding images, please install \'Pillow\'.'			)

     SCREAMING_SNAKE_CASE__					:					Tuple      =		array.dtype
     SCREAMING_SNAKE_CASE__					:					List[str]      =		dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
     SCREAMING_SNAKE_CASE__					:					Dict      =		dtype.kind
     SCREAMING_SNAKE_CASE__					:					Dict      =		dtype.itemsize

     SCREAMING_SNAKE_CASE__					:					Dict      =		None

     # Multi-channel array case (only np.dtype("|u1") is allowed)
     if array.shape[2:]:
          SCREAMING_SNAKE_CASE__					:					List[str]      =		np.dtype('|u1'			)
          if dtype_kind not in ["u", "i"]:
               raise TypeError(
                   f'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'''			)
          if dtype is not dest_dtype:
               warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''			)
    # Exact match
     elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
          SCREAMING_SNAKE_CASE__					:					List[str]      =		dtype
     else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
          while dtype_itemsize >= 1:
               SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		dtype_byteorder + dtype_kind + str(lowercase__			)
               SCREAMING_SNAKE_CASE__					:					Dict      =		np.dtype(lowercase__			)
               if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                    warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''			)
                    break
               else:
                    dtype_itemsize //= 2

     if dest_dtype is None:
          raise TypeError(
              f'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'''			)
     SCREAMING_SNAKE_CASE__					:					List[Any]      =		PIL.Image.fromarray(array.astype(lowercase__			)			)
     return {"path": None, "bytes": image_to_bytes(lowercase__			)}







def      _a					(   lowercase__	:      Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]			):







     '''simple docstring'''


     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('To support encoding images, please install \'Pillow\'.'			)

     if objs:
          SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					str      =		first_non_null_value(lowercase__			)
          if isinstance(lowercase__  ,	lowercase__			):
               return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
          if isinstance(lowercase__  ,	np.ndarray			):
               SCREAMING_SNAKE_CASE__					:					Tuple      =		no_op_if_value_is_null(lowercase__			)
               return [obj_to_image_dict_func(lowercase__			) for obj in objs]
          elif isinstance(lowercase__  ,	PIL.Image.Image			):
               SCREAMING_SNAKE_CASE__					:					Dict      =		no_op_if_value_is_null(lowercase__			)
               return [obj_to_image_dict_func(lowercase__			) for obj in objs]
          else:
               return objs
     else:
          return objs






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowercase			:				Tuple   =     logging.get_logger(__name__)







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[Any]								=	['''pixel_values''']




							def __init__(					self    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = PIL.Image.BICUBIC    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 1 / 2_55    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       None:
														super().__init__(**__UpperCAmelCase						)
														A      :					Any      		=					size if size is not None else {'''height''': 2_56, '''width''': 2_56}
														A      :					Any      		=					get_size_dict(__UpperCAmelCase						)
														A      :					List[Any]      		=					crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														A      :					List[Any]      		=					get_size_dict(__UpperCAmelCase    ,						param_name='''crop_size'''						)

														A      :					Dict      		=					do_resize
														A      :					Tuple      		=					size
														A      :					Union[str, Any]      		=					resample
														A      :					Dict      		=					do_center_crop
														A      :					int      		=					crop_size
														A      :					Union[str, Any]      		=					do_rescale
														A      :					str      		=					rescale_factor
														A      :					Optional[Any]      		=					do_normalize
														A      :					Dict      		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														A      :					Dict      		=					image_std if image_std is not None else IMAGENET_STANDARD_STD




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = PIL.Image.BICUBIC    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														A      :					int      		=					get_size_dict(__UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
														return resize(
														    __UpperCAmelCase    ,						size=(size['''height'''], size['''width'''])    ,						resample=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														A      :					Optional[Any]      		=					get_size_dict(__UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
														return center_crop(__UpperCAmelCase    ,						size=(size['''height'''], size['''width'''])    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       List[str]:
														return rescale(__UpperCAmelCase    ,						scale=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														return normalize(__UpperCAmelCase    ,						mean=__UpperCAmelCase    ,						std=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = ChannelDimension.FIRST    ,						**__UpperCAmelCase    ,						)		->       PIL.Image.Image:
														A      :					Optional[int]      		=					do_resize if do_resize is not None else self.do_resize
														A      :					Optional[Any]      		=					resample if resample is not None else self.resample
														A      :					Tuple      		=					do_center_crop if do_center_crop is not None else self.do_center_crop
														A      :					Optional[Any]      		=					do_rescale if do_rescale is not None else self.do_rescale
														A      :					str      		=					rescale_factor if rescale_factor is not None else self.rescale_factor
														A      :					Tuple      		=					do_normalize if do_normalize is not None else self.do_normalize
														A      :					Optional[int]      		=					image_mean if image_mean is not None else self.image_mean
														A      :					Dict      		=					image_std if image_std is not None else self.image_std

														A      :					List[Any]      		=					size if size is not None else self.size
														A      :					List[Any]      		=					get_size_dict(__UpperCAmelCase						)
														A      :					Dict      		=					crop_size if crop_size is not None else self.crop_size
														A      :					Tuple      		=					get_size_dict(__UpperCAmelCase    ,						param_name='''crop_size'''						)

														A      :					Any      		=					make_list_of_images(__UpperCAmelCase						)

														if not valid_images(__UpperCAmelCase						):
																					raise ValueError(
																					    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																					    '''torch.Tensor, tf.Tensor or jax.ndarray.'''						)

														if do_resize and size is None or resample is None:
																					raise ValueError('''Size and resample must be specified if do_resize is True.'''						)

														if do_center_crop and crop_size is None:
																					raise ValueError('''Crop size must be specified if do_center_crop is True.'''						)

														if do_rescale and rescale_factor is None:
																					raise ValueError('''Rescale factor must be specified if do_rescale is True.'''						)

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('''Image mean and std must be specified if do_normalize is True.'''						)

														# All transformations expect numpy arrays.
														A      :					List[Any]      		=					[to_numpy_array(__UpperCAmelCase						) for image in images]

														if do_resize:
																					A      :					List[str]      		=					[self.resize(image=__UpperCAmelCase    ,						size=__UpperCAmelCase    ,						resample=__UpperCAmelCase						) for image in images]

														if do_center_crop:
																					A      :					Dict      		=					[self.center_crop(image=__UpperCAmelCase    ,						size=__UpperCAmelCase						) for image in images]

														if do_rescale:
																					A      :					Union[str, Any]      		=					[self.rescale(image=__UpperCAmelCase    ,						scale=__UpperCAmelCase						) for image in images]

														if do_normalize:
																					A      :					Tuple      		=					[self.normalize(image=__UpperCAmelCase    ,						mean=__UpperCAmelCase    ,						std=__UpperCAmelCase						) for image in images]

														A      :					Optional[Any]      		=					[to_channel_dimension_format(__UpperCAmelCase    ,						__UpperCAmelCase						) for image in images]

														A      :					List[Any]      		=					{'''pixel_values''': images}
														return BatchFeature(data=__UpperCAmelCase    ,						tensor_type=__UpperCAmelCase						)






